MetaTrader5 MCP Server

from abc import ABC, abstractmethod from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Tuple, Type import pandas as pd import numpy as np @dataclass class ForecastResult: forecast: np.ndarray ci_values: Optional[Tuple[np.ndarray, np.ndarray]] = None # (lower, upper) params_used: Optional[Dict[str, Any]] = None metadata: Optional[Dict[str, Any]] = None class ForecastMethod(ABC): """Abstract base class for all forecasting methods.""" @property @abstractmethod def name(self) -> str: """Unique name of the method.""" ... @property def category(self) -> str: """Category of the method (e.g., 'classical', 'neural', 'ensemble').""" return "unknown" @property def required_packages(self) -> List[str]: """List of Python packages required for this method.""" return [] @property def supports_features(self) -> Dict[str, bool]: """Dictionary of supported features (price, return, volatility, ci).""" return {"price": True, "return": True, "volatility": False, "ci": False} @abstractmethod def forecast( self, series: pd.Series, horizon: int, seasonality: int, params: Dict[str, Any], exog_future: Optional[pd.DataFrame] = None, **kwargs ) -> ForecastResult: """ Generate a forecast. Args: series: Historical time series data. horizon: Number of steps to forecast. seasonality: Seasonality period. params: Method-specific parameters. exog_future: Future exogenous variables (optional). **kwargs: Additional arguments. Returns: ForecastResult object containing the forecast and metadata. """ ... def validate_params(self, params: Dict[str, Any]) -> List[str]: """ Validate method-specific parameters. Args: params: Dictionary of parameters to validate. Returns: List of error messages (empty if valid). """ return []